As illustrated in Figure REF , our network consists of the main segmentation branch and the additional boundary stream that processes the feature maps at the boundary level [1]}. The main branch follows [2]} an asymmetric encoder-decoder structure. The input to the encoder is a 176x176x176 crop which is initially fed into a 3x3x3 convolution with 16 filters. Feature maps are then extracted at each resolution by feeding them into a residual block [3]} followed by a strided 3x3x3 convolution (for downsizing and doubling of feature dimension). The bottom of the encoder entails four consecutive residual blocks that are connected to the decoder. The extracted feature maps in the decoder are upsampled using bilinear interpolation and added with feature maps from the encoder. The output of the decoder is concatenated with the output of the boundary and fed into a 1x1x1 convolution with 2 channels where channel-wise sigmoid activation \(\sigma (X) =\frac{1}{1+\exp ({-X})}\)  determines the probability of each voxel belonging to kidneys and tumor or only tumor classes.
